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[ Daily / Weekly / Overview ]

Oct 03, 2026

General Biomedical Agents

• Biomni (⭐3.9k) — General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks.

• ToolUniverse (⭐1.7k) — Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases.

• BioMedAgent (⭐143) — Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis.

• BioMaster (⭐114) — Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics.

• BRAD (⭐64) — Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows.

Therapeutics & Drug Discovery Agents

• TxAgent (⭐655) — Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis.

• Medea (⭐131) — Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts.

• DrugAgent (⭐1) — Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation.

Bioinformatics & Omics Agents

• STELLA (⭐156) — Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows.

• AutoBA (⭐239) — Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives.

• GenoMAS (⭐135) — Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery.

• BIA (⭐45) — Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers.

• BioAgents (⭐47) — Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting.

• Genomi (⭐483) — Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device.

Multi-Agent Scientific Labs

• Virtual Lab (⭐737) — Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery.

• Agent Laboratory (⭐5.9k) — End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation.

Paper & Workflow Agents

• Paper2Agent (⭐3.6k) — Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools.

Cancer Genome Foundation Models / Protein Structure Prediction and Design

• TESSERA (⭐6) — Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.

Single-Cell Epigenomics Foundation Models / Protein Structure Prediction and Design

• EpiAgent (⭐72) — scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout.

• SCARF — Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation.

• scDNAm-GPT (⭐20) — Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution.

• EpiFoundation — Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction.

• ChromFound (⭐6) — Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction.

• Atacformer — Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping.

• CLM-X — Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction.

Other Omics Foundation Models / Protein Structure Prediction and Design

• CAPTAIN — Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks.

• HiCFoundation — Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation.

• OmicsFM (⭐6) — Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints.

• VirTues (⭐156) — Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging.

Oct 02, 2026

Drug Response & Perturbation

• Chem-PerturBridge (⭐2) — Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels.

• GSE191127 Breast Cancer Pre/Post Chemotherapy — Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes.

• LINCS L1000 Phase 1 (⭐2) — Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes.

• LINCS L1000 Phase 2 (⭐2) — Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes.

• MIX-Seq — Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines.

• Novartis Perturbation Dataset (⭐2) — Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules.

• OP3 (⭐2) — Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts.

• PDX-Atlas — Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models.

• sci-Plex — Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses.

• Tahoe-100M — Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules.

• VCPI-0001 (⭐2) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context.

• VCPI-0002 (⭐2) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context.

Genetic Perturbation & Perturb-seq

• Dixit et al. 2016 Perturb-seq — Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations.

• Adamson et al. 2016 Perturb-seq — CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response.

• Datlinger et al. 2017 CROP-seq — Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation.

• Norman et al. 2019 CRISPRa Perturb-seq — Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations.

• Gasperini et al. 2019 CRISPRi enhancer screen — High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation.

• Replogle et al. 2022 K562 Genome-wide Perturb-seq — Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells.

• Replogle et al. 2022 K562 Essential Perturb-seq — CRISPRi Perturb-seq focused on essential genes in K562 cells.

• Replogle et al. 2022 RPE1 Essential Perturb-seq — CRISPRi Perturb-seq focused on essential genes in RPE1 cells.

• Frangieh et al. 2021 Perturb-CITE-seq — Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions.

Drug Response Prediction

• PASO (⭐8) — Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES.

• DTLCDR — Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.

• THERAPI (⭐1) — Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.

• EXPRESSO — Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.

• PerturbRx — Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.

Drug Perturbation

• Bison — Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision.

• biolord (⭐102) — Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states.

• PerturbNet (⭐69) — Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations.

• CellFlow (⭐160) — Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations.

• Prophet (⭐41) — Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts.

• PrePR-CT (⭐8) — Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction.

• XPert (⭐27) — Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context.

• State (⭐707) — Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells.

• TxPert (⭐52) — Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings.

• LPM (⭐54) — Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations.

Single-cell Foundation Models / Transcriptomics Foundation Models

• CellFM (⭐115) — 800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks.

• scPRINT-2 (⭐39) — Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer.

Single-cell Foundation Models / Pathology Foundation Models

• TITAN (⭐371) — Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.

• Virchow2 — 632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision.

• H-Optimus-0 — 1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus.

• H-Optimus-1 — 1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients.

• UNI2-h — Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks.

• Phikon-v2 — Updated pathology foundation model for general-purpose histology feature extraction and transfer learning.

• GenBio-PathFM (⭐40) — 1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning.

Compound Foundation Models / Compound Embedding

• Uni-Mol2 (⭐1.2k) — Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations.

• ChemFM (⭐87) — 1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks.

Protein Foundation Models / Pre-trained Embedding

• ESM Cambrian (ESM C) (⭐3k) — Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models.

Protein Foundation Models / Protein Structure Prediction and Design

• Boltz-2 (⭐4.2k) — Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening.

• Protenix (⭐2.1k) — Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines.

Cancer Genome Foundation Models / Protein Structure Prediction and Design

• MutationProjector — Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning.

Other Omics Foundation Models / Protein Structure Prediction and Design

• MethylGPT (⭐67) — Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants.

• CpGPT (⭐82) — DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction.

• Casanovo Foundation (⭐1) — Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction.

RNA Foundation Models / Protein Structure Prediction and Design

• RNA-FM (⭐392) — General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning.

• RiNALMo (⭐177) — RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks.

Genomics Foundation Models / Protein Structure Prediction and Design

• Evo 2 (⭐4.2k) — Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution.

• AlphaGenome (⭐2.2k) — Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution.

• modernGENA (⭐232) — ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling.

Aug 31, 2026

Protein Property Prediction

• NbBayesLM (⭐4) — Bayesian neural network integrating protein language model embeddings and physicochemical features to predict nanobody thermostability with uncertainty estimates. Paper

Curation Criteria (Strict) / Protein Structure Prediction and Design

• The resource has clear value to the scope and audience of this collection; highly specialized resources with limited relevance beyond a narrow application context may be declined even when technically sound.

Aug 14, 2026

Genome

• OncoKB — Precision oncology knowledge base of cancer genes, variants, and therapeutic implications.

Aug 08, 2026

Drug Perturbation

• CellOT (⭐182) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.

• CMonge (⭐25) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.

Jul 18, 2026

Preprocessing Tools

• SeqBench — Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.

Jul 16, 2026

Single-Cell & Spatial

• HEST Xenium virtual spatial transcriptomics — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: DeepSpot-M.

• TCGA virtual spatial transcriptomics atlas — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: DeepSpot-M.

Single-cell Foundation Models / Spatial Foundation Models

• DeepSpot (⭐101) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.

• DeepSpot2Cell (⭐21) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).

• DeepSpot-M (⭐58) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

• AESTETIK (⭐28) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.

Jun 20, 2026

Molecular, Protein & Drug Discovery

• Bento (⭐13) — Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.

Preprocessing Tools

• AutoZyme (⭐51) — Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the original results.

Molecular Generation

• Matcha (⭐35) — Multi-stage Riemannian flow matching model for physically valid molecular docking with scoring, pose filtering, and benchmarks.

Bioinformatics & Omics Agents

• CASSIA (⭐107) — Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents.

May 06, 2026

Pathway

• OmniPath — Comprehensive resource integrating protein interactions, signaling pathways, gene regulatory networks, and miRNA targets from over 100 databases.

• SIGNOR 2.0 — Database of causal signaling interactions and pathways, with signed and directed relationships between proteins.

Gene Regulatory Network

• TRRUST v2 — Manually curated database of human and mouse transcriptional regulatory interactions between transcription factors and their target genes, expanded with literature-derived evidence.

Apr 23, 2026

Compound Foundation Models / Compound Embedding

• GROVER (⭐395) — Self-supervised graph transformer for large-scale molecular representation learning from unlabeled compounds.

• Mol2Vec (⭐293) — Unsupervised molecular embedding method inspired by Word2Vec for learning vector representations of chemical substructures.

• MolFormer (⭐415) — Linear attention transformer pretrained on millions of SMILES strings for efficient molecular embeddings.

• Uni-Mol (⭐1.2k) — 3D molecular pretraining framework for universal representation learning on molecules and protein pockets.

Apr 20, 2026

Molecular, Protein & Drug Discovery

• DUD-E (Directory of Useful Decoys, Enhanced) — Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.

Apr 17, 2026

Drug Response & Perturbation

• JUMP Cell Painting Datasets (⭐190) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.

• scPerturb (⭐189) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.

Drug Perturbation

• chemCPA (⭐160) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.

• cycleCDR (⭐5) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.

Apr 16, 2026

GitHub Pages UI

• Search matches name, description, tasks, modalities, and tags.

• The Task, Modality, and Type filters map directly to tasks, modalities, and type in docs/data/resources.json.

• Clicking badges on cards applies the corresponding filter.

Curation Criteria (Strict) / Protein Structure Prediction and Design

• The resource is trustworthy and relevant to computational biology.

• The primary link points to an official source (official docs, organization site, maintained repository, or official dataset page).

• The resource has evidence of technical substance: ideally a peer-reviewed paper; at minimum a preprint or official technical documentation.

• The description is factual and concise (no marketing copy).

• Duplicate or near-duplicate entries should be avoided.

Update & Link Rot Policy / Protein Structure Prediction and Design

• Link validity is monitored by the Link Check workflow.

• If a link repeatedly fails, maintainers may replace it with an official mirror/canonical URL or remove the entry until a stable URL is available.

• Contributions fixing broken links are welcome and encouraged.

Data Schema & Contribution Workflow / Protein Structure Prediction and Design

• Data schema reference: docs/data/SCHEMA.md.

• Source-of-truth workflow:
  1. Edit/add resources in README.md.
  2. Regenerate machine-readable artifacts:
     • python scripts/sync_resources_from_readme.py
     • python scripts/build_resources.py
  3. Commit updated data files (data/resources.yml, data/resources.json, data/resources.csv, docs/data/resources.json) with your README change.

• Contribution guide: contributing.md.

Apr 15, 2026

Preprocessing Tools

• Numbat (⭐230) — Haplotype-aware copy number variation inference from single-cell RNA-seq using hidden Markov models.

• CaSpER (⭐92) — CNV identification and visualization by integrative analysis of single-cell or bulk RNA-seq data.

• CellCharter (⭐197) — Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian mixture models.

• STAGATE (⭐54) — Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.

• NCEM (⭐121) — GNN-based model for learning intercellular communication from spatial graphs of cells.

• DeepTalk (⭐30) — Graph attention network for deciphering cell-cell communication from spatial transcriptomics.

• COMMOT (⭐146) — Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.

• TIGON (⭐60) — Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.

• LINGER (⭐140) — Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data pretraining.

• sciPENN (⭐19) — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.

• MOGONET (⭐188) — Multi-omics graph convolutional network framework for patient classification and biomarker identification.

Drug Response Prediction

• DRUML (⭐12) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.

Drug Perturbation

• PRNet (⭐91) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.

Drug Repurposing

• TranSiGen (⭐39) — Dual-VAE architecture for ligand-based virtual screening, drug response prediction, and drug repurposing using chemical-induced transcriptional profiles.

Molecular Generation

• DiffSBDD (⭐531) — Equivariant diffusion model for structure-based drug design that generates molecules and binding conformations for protein targets.

• ReLeaSE (⭐373) — Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.

• PaccMannRL (⭐11) — Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.

Single-cell Foundation Models / Transcriptomics Foundation Models

• SATURN (⭐174) — Transformer-based model integrating gene expression and protein sequences via a protein language model to learn unified multi-species cell embeddings.

• CancerFoundation (⭐32) — Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn cancer-specific embeddings.

Single-cell Foundation Models / Spatial Foundation Models

• Nicheformer (⭐175) — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.

• scGPT-spatial (⭐142) — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.

Single-cell Foundation Models / Multi-Omics Foundation Models

• Concerto (⭐41) — Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and reference-query mapping.

• scButterfly (⭐30) — Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics data.

• JAMIE (⭐17) — Joint variational autoencoder for multimodal single-cell data imputation and embedding.

• scPair (⭐11) — Bidirectional feedforward network for single-cell multimodal analysis with cross-modality prediction leveraging single-cell atlases.

Multi-Modal Foundation Models / Protein Structure Prediction and Design

• PORPOISE (⭐252) — Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.

• PathomicFusion (⭐327) — Integrated framework fusing histopathology and genomic features via CNN, GNN, and attention gating for cancer diagnosis and prognosis.

• Virchow — Million-slide digital pathology foundation model using a vision transformer and self-supervised distillation for tile-level pathology image representation.

• TOAD (⭐186) — Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.

• PLIP (⭐380) — Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for image classification and text-to-image retrieval.

• MUSK (⭐247) — Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for biomarker prediction and retrieval.

Mar 31, 2026

Compound

• DrugTargetCommons — Community platform for curating and integrating experimental bioactivity data across drugs and targets.

Protein

• UniRef — Non-redundant sequence database clustering UniProtKB entries at multiple sequence identity thresholds.

• InterPro — Protein families, domains, and functional sites database integrating 14 member databases including Pfam and PROSITE.

• Pfam — Database of protein families described by multiple sequence alignments and hidden Markov models.

• NeXtProt — Expert knowledge base on human proteins with deep functional annotation, complementary to UniProt.

Genome

• ROADMAP Epigenomics — Reference epigenome maps for 111 primary human cell types and tissues, including histone modifications, chromatin accessibility, and DNA methylation.

• FANTOM5 — Functional annotation of mammalian genome; comprehensive atlas of active enhancers, promoters, and transcription start sites across human and mouse cell types.

Disease

• Open Targets Platform — Systematic target identification and prioritization platform integrating genetics, genomics, and drug data for drug discovery.

• Human Phenotype Ontology (HPO) — Standardized vocabulary of phenotypic abnormalities in human disease, linking genes, variants, and clinical features.

• DISEASES — Gene–disease association database integrating evidence from text mining, curated databases, and experimental data.

Chemical-Protein Interaction

• Davis kinase inhibitors DB — Experimental kinase inhibitor binding affinity dataset for protein–ligand interaction research.

• Kinase Inhibitor Bioactivity Data (KIBA) — Integrated bioactivity scores for kinase inhibitors combining Ki, Kd, and IC50 measurements.

Gene Regulatory Network

• RegNetwork — Database of gene regulatory networks covering transcription factor–target gene and miRNA–gene interaction data across multiple species.

• miRBase — Reference repository for microRNA gene annotations, sequences, and experimentally validated targets.

Molecular, Protein & Drug Discovery

• PK-DB — Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.

Preprocessing Tools

• STAR (⭐2.3k) — Ultrafast universal RNA-seq aligner with support for spliced alignment and single-cell quantification via STARsolo.

• kallisto — Near-optimal RNA-seq quantification using pseudoalignment for fast transcript abundance estimation.

• Harmony (⭐674) — Fast and scalable integration of single-cell data across datasets, conditions, technologies, and species.

• Monocle3 — Single-cell trajectory analysis tool for learning developmental trajectories and ordering cells in pseudotime.

• CellChat (⭐791) — Inference and analysis of cell-cell communication ligand-receptor networks from single-cell transcriptomics data.

• SCENIC (⭐498) — Single-cell regulatory network inference and clustering linking transcription factors to co-expressed gene modules.

• DoubletFinder (⭐569) — Machine learning approach for detecting multiplet (doublet) artifacts in single-cell RNA-seq data.

Drug Response Prediction

• TGSA (⭐24) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.

• HiDRA — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.

Molecular Generation

• JTVAE (⭐566) — Junction tree variational autoencoder for molecular graph generation that guarantees chemical validity via a hierarchical tree decomposition.

LLM for Biology

• MolT5 (⭐195) — Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule generation.

• ChatDrug (⭐163) — LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and optimization.

Protein Foundation Models / Pre-trained Embedding

• ProtTrans (⭐1.3k) — Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef and BFD.

• ProGen2 (⭐705) — Protein language model trained on diverse protein families for sequence generation and fitness prediction.

• Ankh (⭐250) — Efficient protein language model optimized for downstream prediction tasks including secondary structure, localization, and function annotation.

Genomics Foundation Models / Protein Structure Prediction and Design

• DeepSEA — Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity across thousands of chromatin features.

• Sei (⭐118) — Sequence-to-function framework learning a genome-wide regulatory activity code from DNA sequences for variant effect prediction.

• GPN (Genomic Pre-trained Network) (⭐382) — Masked language model for DNA sequences enabling zero-shot variant effect prediction without requiring functional annotations.

Mar 13, 2026

Protein Foundation Models / Protein Structure Prediction and Design

• EvoDiff (⭐686) — Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ paper-2023 ]

Mar 11, 2026

Drug Response & Perturbation

• BEAT AML — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.

• LINCS L1000 — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.

• PharmGKB — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.

• PRISM — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.

Single-Cell & Spatial

• scIB (Single-cell Integration Benchmarks) (⭐432) — Comprehensive benchmarking framework for single-cell data integration methods.

• Tabula Muris — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.

• Tabula Sapiens — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.

Molecular, Protein & Drug Discovery

• BACE — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.

• ClinTox — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.

• FLIP (Fitness Landscape Inference for Proteins) (⭐141) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.

• ProteinGym (⭐474) — Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.

• QM9 — Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.

• SIDER (Side Effect Resource) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.

• TAPE (Tasks Assessing Protein Embeddings) (⭐745) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.

• Tox21 — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.

Genomics, Cancer & Biomedical Cohorts

• 1000 Genomes Project — Reference panel of human genetic variation from 2,504 individuals across 26 populations.

• CPTAC (Clinical Proteomic Tumor Analysis Consortium) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.

• The Cancer Genome Atlas (TCGA) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.

• UK Biobank — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.

General Biological ML & Knowledge Graph Benchmarks

• OGB (Open Graph Benchmark) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.

Mar 10, 2026

Preprocessing Tools

• scVelo (⭐512) — RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.

Drug Response Prediction

• RECOVER (⭐26) — Machine learning framework for predicting synergistic drug combination responses across cell lines.

Molecular Generation

• DiffDock (⭐1.6k) — Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.

LLM for Biology

• BioMedLM — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.

Single-cell Foundation Models / Transcriptomics Foundation Models

• UCE (⭐345) — Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and assays without fine-tuning.

• GEARS (⭐414) — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.

Protein Foundation Models / Protein Structure Prediction and Design

• OpenFold (⭐3.4k) — Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction workflows.

• SaProt — Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry for improved function prediction.

Genomics Foundation Models / Protein Structure Prediction and Design

• Borzoi (⭐265) — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution.

Mar 02, 2026

scRNA

• CZ CELLxGENE — Single-cell dataset repository and interactive explorer from the Chan Zuckerberg Initiative.

• Human Cell Atlas — Open global atlas of all cells in the human body.

Compound

• HMDB (Human Metabolome Database) — Comprehensive database of small molecule metabolites found in the human body.

• DrugCentral — Online drug compendium with drug mode of action and indication information.

Protein

• SAbDab — Structural Antibody Database containing all antibody structures in the PDB.

• OADB (Observed Antibody Space Database) — Database of antibody sequences from immune repertoire sequencing.

Genome

• ENCODE — Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.

• Ensembl — Genome browser and annotation database for vertebrate and other eukaryotic genomes.

• gnomAD — Genome Aggregation Database; genetic variation from large-scale sequencing projects.

• Rfam — Database of RNA families with sequence alignments and consensus structures.

Disease

• DisGeNET — Database of gene-disease associations integrating expert-curated and GWAS data.

• OMIM (Online Mendelian Inheritance in Man) — Comprehensive database of human genes and genetic disorders.

Protein-Protein Interaction

• IntAct — Open-source molecular interaction database and analysis system from EMBL-EBI.

Drug Response & Perturbation

• Cancer Therapeutics Response Portal (CTRP) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.

Molecular, Protein & Drug Discovery

• BindingDB Curated Sets — Curated binding affinity datasets for protein–ligand interaction benchmarking.

• GuacaMol (⭐534) — Benchmark suite for generative molecular design models.

• MOSES (⭐991) — Benchmarking platform for molecular generation models.

• Therapeutics Data Commons (TDC) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.

Preprocessing Tools

• Biopython — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.

• DeepChem (⭐7k) — Deep learning library for drug discovery, quantum chemistry, and materials science.

• scvi-tools — Probabilistic models for single-cell omics data analysis.

• CellTypist (⭐506) — Automated cell type annotation for scRNA-seq.

• GROMACS — Molecular dynamics simulation package for biochemical molecules.

• MDAnalysis — Python library for analyzing and altering molecular dynamics simulation trajectories.

• OpenMM — High-performance toolkit for molecular simulation and GPU-accelerated MD.

Molecular Generation

• REINVENT (⭐377) — Reinforcement learning for de novo drug design.

• MolGPT (⭐178) — Transformer-based model for molecular generation.

• Molecular Transformer (⭐429) — Sequence-to-sequence model for retrosynthesis prediction.

• TargetDiff (⭐347) — 3D equivariant diffusion model for structure-based drug design.

General Biomedical Agents

• ClawBio (⭐1.1k) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.

Single-cell Foundation Models / Transcriptomics Foundation Models

• Geneformer — Context-aware, attention-based deep learning model pretrained on a large corpus of single-cell transcriptomes.

• scBERT (⭐364) — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.

• CellPLM (⭐107) — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.

Single-cell Foundation Models / Spatial Foundation Models

• GigaPath (⭐638) — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.

• UNI (⭐784) — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.

• CONCH (⭐534) — Vision-language foundation model for computational pathology trained with contrastive captioning on pathology image–text pairs.

• Phikon — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.

Single-cell Foundation Models / Multi-Omics Foundation Models

• scMulan (⭐63) — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.

• totalVI (⭐1.7k) — Probabilistic framework for joint analysis of paired scRNA-seq and protein (CITE-seq) data enabling multi-modal cell state representation across single-cell datasets.

• MultiVI (⭐1.7k) — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.

• MIRA (⭐70) — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.

• GLUE (⭐480) — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.

• BABEL (⭐53) — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.

• Multigrate (⭐35) — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.

• MOFA+ (⭐426) — Multi-Omics Factor Analysis framework identifying shared axes of variation across bulk and single-cell datasets including RNA, ATAC, proteomics, methylation, and copy number.

• GeneCompass (⭐124) — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.

• UnitedNet (⭐53) — Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics, chromatin accessibility, and proteomics.

• SpatialGlue — Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.

• MIDAS (⭐72) — Mosaic integration and differential accessibility model for single-cell multi-omics that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.

Single-cell Foundation Models / Domain Alignment

• scArches (⭐411) — Transfer learning framework for mapping new single-cell datasets onto pre-trained reference atlases across batches, conditions, and modalities.

• TOSICA — Transformer-based framework for one-stop interpretable cell-type annotation supporting cross-dataset and cross-species transfer.

Protein Foundation Models / Protein Structure Prediction and Design

• AlphaFold3 (⭐8.6k) — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.

• Boltz-1 (⭐4.2k) — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.

• Chai-1 (⭐2k) — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.

• ESM3 (⭐3k) — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.

• ESMFold (⭐4.2k) — Fast protein structure prediction using language model embeddings.

• RFdiffusion (⭐3.1k) — Generative model for protein backbone design using diffusion.

• ProteinMPNN (⭐1.9k) — Deep learning model for protein sequence design given backbone structure.

• OmegaFold (⭐627) — High-resolution de novo protein structure prediction from sequence.

• RoseTTAFold (⭐2.3k) — Three-track neural network for protein structure prediction.

Multi-Modal Foundation Models / Protein Structure Prediction and Design

• CHIEF (⭐719) — Clinical Histopathology Imaging Evaluation Foundation model integrating histology images and clinical context for pan-cancer analysis.

• BiomedCLIP — CLIP-based vision-language foundation model for biomedical images and text trained on PubMed figure–caption pairs.

Genomics Foundation Models / Protein Structure Prediction and Design

• Nucleotide Transformer (⭐923) — Foundation model for genomic sequences across multiple species.

• DNABERT (⭐780) — Pre-trained bidirectional encoder for DNA sequence analysis.

• DNABERT-2 — Improved genome foundation model with efficient tokenization.

• Enformer (⭐15k) — Transformer model predicting gene expression from DNA sequence.

• Basenji (⭐475) — Sequential regulatory activity prediction from DNA sequences.

• Caduceus (⭐252) — Bidirectional equivariant long-range DNA sequence model based on Mamba.

• Evo (⭐1.6k) — Long-context genomic foundation model (up to 1M tokens).

• HyenaDNA (⭐809) — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.

Jan 29, 2026

Pathway

• Reactome — Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.

• BioCyc — Collection of pathway/genome databases across thousands of organisms.

• MSigDB (Molecular Signatures Database) — Curated gene sets derived from pathways and biological processes.

Protein

• PROTEIN DATA BANK (PDB) — 3D structures of proteins, nucleic acids, complexes.

• RCSB Protein Data Bank — Repository for structural data of biological molecules.

Disease

• DrugBank — Database of drugs and targets (University of Alberta).

Drug-Gene Interaction

• Comparative Toxicogenomics Database — Chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype associations.

• SNAP — Dataset of drug-gene interactions.

Drug Response & Perturbation

• Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.

Drug (Cell Line) Response

• Cancer Cell Line Encyclopedia — Database of ~1000 cancer cell lines.

• CellMiner Cross Database (CellMinerCDB) — Integrates multiple cancer cell line databases.

Chemical-Protein Interaction

• BindingDB — Compounds and target database.

• PDBBind — Binding affinity data for biomolecular complexes.

Protein-Protein Interaction

• BioGRID — Protein, genetic, and chemical interactions.

• HIPPIE — Human protein-protein interaction database.

Knowledge Graph

• DRKG (⭐704) — Large-scale biological knowledge graph for drug discovery.

• Hetionet (⭐363) — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.

• PrimeKG (⭐831) — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.

Molecular, Protein & Drug Discovery

• CrossDocked2020 — Large-scale dataset for structure-based virtual screening.

General Biological ML & Knowledge Graph Benchmarks

• OpenBioLink (⭐164) — Benchmark datasets for biological knowledge graph completion.

API

• PubMed E-utilities (esearch/efetch) — APIs for searching and retrieving biomedical literature from PubMed.

• NCBI E-utilities — Unified APIs for accessing NCBI databases (Gene, GEO, SRA, PubChem, etc).

• UniProt REST API — Programmatic access to protein sequence and functional annotation data.

• Ensembl REST API — API for genomic annotations, variants, genes, and comparative genomics.

• KEGG REST API — API for accessing KEGG pathways, compounds, genes, and reactions.

• ChEMBL Web Services — REST API for bioactive molecules, targets, and bioassays.

• Open Targets Platform API — API for target–disease associations integrating genetics, genomics, and drug data.

• ClinicalTrials.gov API — API for querying clinical trial metadata and results.

Drug Target Interaction

• DTINet (⭐191) — Network-based framework integrating heterogeneous biological data for DTI prediction.

• DeepDTA (⭐306) — Deep learning model using CNNs on protein sequences and drug SMILES.

• GraphDTA (⭐308) — Graph neural network–based DTI prediction using molecular graphs.

• MolTrans (⭐248) — Transformer-based DTI model leveraging molecular substructures.

• DrugBAN (⭐154) — Bilinear attention network for interpretable DTI prediction.

Protein Foundation Models / Pre-trained Embedding

• Evolutionary Scale Modeling (ESM) (⭐4.2k) — Protein embeddings.

Jan 07, 2026

Preprocessing Tools

• ChatSpatial (⭐45) — MCP server for spatial transcriptomics analysis via natural language.

Single-cell Foundation Models / Transcriptomics Foundation Models

• scFoundation (⭐431) — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.

• scGPT (⭐1.6k) — Transformer-based foundation model pretrained on millions of single-cell profiles.

• BulkFormer (⭐85) — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.

Jan 02, 2026

Preprocessing Tools

• FlashDeconv (⭐26) — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).

• Squidpy — Python library for spatial single-cell analysis.

Nov 17, 2024

Drug Response Prediction

• MOFGCN (⭐8) — GCN + heterogeneous network.

• DeepDSC — Autoencoder + fully connected NN.

• DGDRP (⭐1) — Multi-view embedding neural network.

• DeepAEG (⭐4) — GNN embedding + attention mechanism.

Aug 31, 2024

Compound

• ZINC ligand discovery database — Free database of commercially-available compounds for virtual screening.

Protein

• Critical Assessment of Structure Prediction (CASP) — Assessing methods for protein structure prediction.

• Uniclust — Clustered protein sequence databases.

• CATH database — Hierarchical classification of protein domain structures.

Genome

• 10x Genomics Dataset — Collection of single-cell datasets.

• The Genotype-Tissue Expression (GTEx) — Human gene expression and regulation resource.

• Dependency Map (DepMap) — CRISPR-Cas9 screens in cancer cell lines.

• Catalogue Of Somatic Mutations In Cancer (COSMIC) — Resource on somatic mutations in cancers.

• MGnify — Resource for metagenomic and metatranscriptomic data.

• JASPAR — Database of transcription factor binding profiles.

Drug Response & Perturbation

• NCI60 — Drug sensitivity benchmark across 60 diverse human cancer cell lines.

Chemical-Protein Interaction

• STITCH — Chemical-protein interactions.

Clinical Trial

• ClinicalTrials.gov — Privately and publicly funded clinical studies.

• ICD10 — International Classification of Diseases, 10th revision.

• EU Drug Regulating Authorities Clinical Trials DB (EudraCT) — European clinical trial database.

• MIMIC-IV — Freely accessible critical care database.

Molecular, Protein & Drug Discovery

• MoleculeNet — Benchmark datasets for molecular machine learning.

Aug 10, 2024

Compound

• Therapeutic Target Database — Drug-target, target-disease, and drug-disease datasets.

Knowledge Graph

• Drug Mechanism Database (DrugMechDB) (⭐79) — Mechanisms of action from drug to disease.

Drug Repurposing

• DeepPurpose (⭐1.2k) — Deep learning library for drug repurposing.

Aug 09, 2024

LLM for Biology

• scPRINT (⭐162) — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.

Jul 16, 2024

Drug Response Prediction

• drGAT (⭐2) — Attention-based model for drug response prediction with gene explainability.

LLM for Biology

• GeneGPT (⭐431) — LLM for biomedical information, integrated with various APIs.

• GenePT (⭐324) — Foundation LLM for single-cell data.

Mar 16, 2024

Compound-Protein Interaction

• TransformerCPI (⭐159) — CPI prediction using Transformer.

LLM for Biology

• AI4Chem/ChemLLM-7B-Chat — LLM for chemical & molecular science.

• BioGPT (⭐4.5k) — LLM for biomedical text generation.

Compound Foundation Models / Compound Embedding

• ChemBERTa-2 (⭐501) — RoBERTa-based molecular language model pretrained on SMILES for small-molecule representation learning.

Nov 28, 2023

Compound

• Drug Repurposing Hub — Collections of drug repurposing data (drug, MoA, target, etc).

Protein

• AlphaFold Protein Structure Database — 3D protein structure predictions.

Protein-Protein Interaction

• STRING — PPI networks for multiple organisms.

Sep 07, 2023

Compound

• Rhea — Database of chemical reactions.

Jun 13, 2023

scRNA

• Single Cell Expression Atlas — Public database for single-cell RNA.

Pathway

• PathwayCommons — Database of pathways and interactions.

Genome

• cBioPortal — Cancer genomics database; aggregating many patient datasets.

Drug-Gene Interaction

• DGIdb — Drug-gene interactions and the druggable genome.

Preprocessing Tools

• Scanpy — Python library for scRNA-seq analysis.

• Seurat — R library for scRNA-seq analysis.

Apr 16, 2023

scRNA

• Gene Expression Omnibus — Public functional genomics database.

• Single Cell PORTAL — Public database for single-cell RNA.

May 18, 2022

Compound

• ChEBI — Database focused on small chemical compounds.

May 15, 2022

Compound

• PubChem — One of the largest chemical databases (compounds, genes, and proteins).

• ChEMBL — Bioactive molecules with drug-like properties.

• ChemSpider — Chemical structure database.

• KEGG COMPOUND — Collection of small molecules and biopolymers.

• LIPID MAPS — Database of lipids.

Pathway

• KEGG PATHWAY — Collection of pathway maps.

• WikiPathways — Database of biological pathways.

Mass Spectra

• MassBank — Open source databases and tools for mass spectrometry reference spectra.

• MoNA MassBank of North America — Meta-database of metabolite mass spectra, metadata, and associated compounds.

Protein

• THE HUMAN PROTEIN ATLAS — Comprehensive human protein database (cells, tissues, organs).

• UniProt — Functional information on proteins.

Genome

• Human Genome Resources at NCBI — Database for genomics, proteomics, transcriptomics, and systems biology.

• GenBank — NCBI's database of genetic sequences.

• UCSC Genome Browser — UCSC's genome browser.

Disease

• KEGG DRUG — Comprehensive, approved drug information.

Preprocessing Tools

• Chemistry Development Kit (⭐606) — Cheminformatics software & machine learning tools.

• RDKit (⭐3.6k) — Cheminformatics software & machine learning toolkit.

Drug Target Interaction

• NeoDTI (⭐78) — Library for drug-target interaction prediction.

Compound-Protein Interaction

• MCPINN (⭐4) — Drug discovery via compound-protein interaction and machine learning.